Table 1 Examples of AI tools in drug discovery

IBM Watson for drug discovery

IBM Watson for Drug Discovery leverages AI to analyze biomedical literature, clinical trial data, and other relevant information to help researchers identify potential drug candidates and biomarkers

Chen et al. [10] and Hatz et al. [18]

Atomwise

Atomwise uses deep learning for the virtual screening of potential drug compounds. It analyzes molecular structures to predict their binding affinity with target proteins, expediting the identification of potential drug candidates

Carpenter and Huang [8]

DeepChem

DeepChem is an open-source platform that provides a collection of deep-learning tools for drug discovery

Korshunova et al. [23]

In silico medicine

In silico medicines aims to provide aging research and medicine discovery by applying AI. It also utilizes biological data and seeks promising medication options that focus on sickness, which comprises neurological conditions and cancer

Shaker et al. [41]

Recursion pharmaceuticals

Recursion Pharmaceuticals screens and analyzes biological pictures with artificial intelligence (AI) to find possible medication prospects. Their approach enables rapid screening of biological abnormalities by combining machine learning and computer vision

Malandraki-Miller and Riley [31]

OpenEye scientific software

OpenEye provides a suite of AI tools for cheminformatics research and structural architecture. Their software helps with drug candidate optimization, biological attribute forecasting and chemical-based dataset assessment

Cox and Gupta [11] and Rifaioglu et al. [38]

Schrodinger

Schrodinger offers a system for molecular interaction modeling, digital screening, and discovery of medicines package that integrates AI. It helps scientists optimize tiny compounds to create medications and estimate binding capacities

Adelusi et al. [1]